Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

A survey on object detection in optical remote sensing images

In this paper, we examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. We first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, we show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows us to reveal the strong polarization dependency of the interferometric coherence. We then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, we demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. We investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

Polarimetric SAR interferometry

To improve further the accuracy in the approximation of the range history for a synthetic aperture radar (SAR) onboard a medium-earth-orbit (MEO) satellite a fourth power term has been added to the advanced hyperbolic range equation (AHRE), and the new one is called a modified AHRE (MAHRE). Then, a two-dimensional spectrum using the MAHRE was derived, and the accuracy of the spectrum analysed. Promising results were obtained.

Two-dimensional spectrum for MEO SAR processing using a modified advanced hyperbolic range equation

The deceptive jamming technique can threaten a synthetic aperture radar (SAR) imaging system by repeatedly transmitting copied radar signals back to the radar. A conventional phased array can generate only an angle-dependent beam and cannot distinguish the jammer from other targets. Instead, a frequency diverse array (FDA) can offer a range-angle-dependent beampattern by introducing small frequency offsets to array elements, which can be adopted to distinguish signals from different range cells. Thus, considering that the position of the jammer is unknown, it is necessary to suppress the range peak sidelobe as much as possible. To lower this value, this communication proposes a weighted FDA to achieve the optimal solution at the expense of raising other range sidelobes.

A Low Sidelobe Deceptive Jamming Suppression Beamforming Method With a Frequency Diverse Array

As an active microwave device, synthetic aperture radar (SAR) uses the backscatter of objects for imaging. SAR image ship targets are characterized by unclear contour information, a complex background and strong scattering. Existing deep learning detection algorithms derived from anchor-based methods mostly rely on expert experience to set a series of hyperparameters, and it is difficult to characterize the unique characteristics of SAR image ship targets, which greatly limits detection accuracy and speed. Therefore, this paper proposes a new lightweight position-enhanced anchor-free SAR ship detection algorithm called LPEDet. First, to resolve unclear SAR target contours and multiscale performance problems, we used YOLOX as the benchmark framework and redesigned the lightweight multiscale backbone, called NLCNet, which balances detection speed and accuracy. Second, for the strong scattering characteristics of the SAR target, we designed a new position-enhanced attention strategy, which suppresses background clutter by adding position information to the channel attention that highlights the target information to more accurately identify and locate the target. The experimental results for two large-scale SAR target detection datasets, SSDD and HRSID, show that our method achieves a higher detection accuracy and a faster detection speed than state-of-the-art SAR target detection methods.

/pdf/a-lightweight-position-enhanced-anchor-free-algorithm-for-14zf7f4r.pdf

A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection

A novel velocity ambiguity resolving method is proposed for moving target indication, in which compressed sensing (CS) is applied to recover the unambiguous Doppler spectrum of targets from randomly pulse repetition frequency-jittering pulses. Weighting in CS recovery is utilised to suppress the clutter by using prior knowledge of the clutter spectrum. Experimental results demonstrate the effectiveness of the proposal.

Velocity ambiguity resolving for moving target indication by compressed sensing

The compact polarimetric (CP) synthetic aperture radar (SAR) data is directly decomposed into three canonical components involving surface, double-bounce and volume, which avoids the approximated quad-polarimetric reconstruction based on several promising assumptions. Through several algebraic operations, the three-component decomposition under CP modes (
 π
/4 mode and right circular transmit, linear receive (CTLR) mode) is deduced. With the experiments by using two classical data sets - San Francisco data from AIRSAR system and Oberpfaffenhofen data from ESAR system, the feasibility and flexibility of the CP decomposition are validated. Also, it has been found that the use of CP data can be further extended into many other polarimetric applications, and the three-component decomposition can achieve a better result with CTLR mode than with π
/4 mode.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2013.0114

Mengdao Xing

Papers

Two-dimensional spectrum for MEO SAR processing using a modified advanced hyperbolic range equation

A Low Sidelobe Deceptive Jamming Suppression Beamforming Method With a Frequency Diverse Array

A Lightweight Position-Enhanced Anchor-Free Algorithm for SAR Ship Detection

Velocity ambiguity resolving for moving target indication by compressed sensing

Analysis of three-component decomposition to compact polarimetric synthetic aperture radar